Sum over rows in scipy.sparse.csr_matrix

Question

I have a big csr_matrix and I want to add over rows and obtain a new csr_matrix with the same number of columns but reduced number of rows. (Context: The matrix is a documen

Answer 1

The indexing should be:

idx1 = [0, 3]       # rows 1 and 4
idx2 = [1, 2, 4]    # rows 2,3 and 5

Then you need to keep A_sub1 and A_sub2 in sparse format and use axis=0:

A_sub1 = csr_matrix(A[idx1, :].sum(axis=0))
A_sub2 = csr_matrix(A[idx2, :].sum(axis=0))
B = vstack((A_sub1, A_sub2))
B.toarray()
array([[5, 0, 0, 0, 0],
       [0, 5, 5, 0, 0]])

Note, I think the A[idx, :].sum(axis=0) operations involve conversion from sparse matrices - so @Mr_E's answer is probably better.

Alternatively, it works when you use axis=0 and np.vstack (as opposed to scipy.sparse.vstack):

A_sub1 = A[idx1, :].sum(axis=0)
A_sub2 = A[idx2, :].sum(axis=0)
np.vstack((A_sub1, A_sub2))

Giving:

matrix([[5, 0, 0, 0, 0],
        [0, 5, 5, 0, 0]])